VLDB2005

CMS-ToPSS: Efficient Dissemination of RSS Documents

Milenko Petrovic Haifeng Liu Hans-Arno JacobsenUniversity of Toronto

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Information Dissemination

Easy to use web publishing tools (blog, wiki) are fueling the increase in the number of web publishers

RSS frequently used to disseminate update to interested users CNN.com, Yahoo! News, Amazon.com, MSN search (beta)

RSSaggregator

RSSreaders

RSSpublishers

Problem: Polling based architecture

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Solution!

Current rss dissemination architecture

G-ToPSS rss dissemination architecture

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Interaction Model: Publish/Subscribe

Broker

Publisher Publisher

Subscriber Subscriber

RSS feeds

MatchingRSS feeds

MatchingRSS feeds

Queries over all RSS

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Research challenges

1. Need a subscription (query) language suitable for filtering of rss documents

2. Need an efficient matching algorithm based on graph representation• Structurally matching• Constraint matching

3. Scalability to a large number of subscriptions and high publishing rate

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CMS-ToPSS System Architecture

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vary number of subs

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Subscription Scalability

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Memory Scalability

memory vs. #subs

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Matching Semantics

PAPER17

“Arno Jacobsen”

AUTHOR

SIGMOD

CONFERENCE

“California”

LOCATION“2001”

YEAR

?y(?y <= Publication)

“Arno Jacobsen”

AUTHOR

SIGMOD

CONFERENCE

?z(?z > 2000)

YEAR

Publication

Subscription

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Data Model (RSS Documents) Publications are represented as directed

graphs with node and edge labels Node labels are typed

Literal value Class

Edge labels are typed Class

Classes can be related using multiple inheritance ontology

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Query Language (GQL)

Queries are represented as directed graph patterns with node and edge labels

Node labels are variables Variables can be constrained by

Classes Class instances and literal values

Edge labels are class instances Mapping (matching) semantics

Pattern graph maps to data graph if the topology (structure) of the two graphs matches and all variable constraints are satisfied

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Conclusion and Future Work

Proposed a prototype for graph-based metadata filtering

G-ToPSS supports high matching rate for an expressive subscription language

Extend G-ToPSS with full RDF language features

Optimize constraint processing during matching